Prior methods and comparisons.
We compare FLAP to four other approaches. Model-Free is broadly representative of prior work that learns a goal-conditioned policy by directly feeding the final goal without using any subgoals  [1]}, [2]}, [3]}.
LEAP [4]}, GCP [5]}, and PTP [6]} learn to plan subgoals in the original high-dimensional state space, instead of a learned lossy representation space as in our method.
We also compare our full model Ours (Broad Data), which is pre-trained on the previously discussed diverse prior datasets, with an ablation Ours (Target Data), which is trained only on a subset of that data from our target domain.
This baseline allows us to evaluate the importance of pre-training on a large and diverse dataset. All methods use the same neural network architectures, and all methods except for the ablation are pre-trained on the same prior data.
